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Transductive Learning

In this setting, both a labeled training sample and an (unlabeled) test sample are provided at training time. The goal is to predict only the labels of the given test instances as accurately as possible.

Papers

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Showing 111–120 of 135 papers

TitleStatusHype
Cross-Graph Learning of Multi-Relational Associations—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
DC Proximal Newton for Non-Convex Optimization Problems—0
Deep Domain Adaptation under Deep Label Scarcity—0
Deep Transductive Semi-supervised Maximum Margin Clustering—0
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents—0
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification—0
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph—0
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